At Altertable, we're building a data operating system. The premise is simple: today's data stack is over-engineered, under-integrated, and ultimately reactive. We want to flip that. We're creating a unified system where AI-native infrastructure, real-time context, and autonomous intelligence work together to deliver continuous insight, without the sprawl.
Here's what that means under the hood.
1. Warehousing That's Ready When You Are
Most companies have data. But turning that data into something usable usually involves a gauntlet of tooling — connectors, batch jobs, orchestration layers, semantic models — all before anyone can ask a meaningful question. We're collapsing that path.
Altertable includes native warehousing capabilities designed for immediate usability. You can push your data directly into Altertable, or connect your own infrastructure and let us query across it. For customers who already have established pipelines and warehouses, we support federated querying and metadata introspection, letting you connect to existing stores without data duplication or rebuilds.
Under the hood, we manage open formats and infrastructure that allow fast startup, flexible schema evolution, and clean lineage, without requiring you to think about storage internals. For modern data teams, that means faster onboarding and fewer architectural constraints, whether you're starting fresh or layering on top of an existing stack.
2. A Unified, AI-Native Runtime
At the core of Altertable is a runtime designed not just for queries, but for understanding. It integrates the semantics of your business with the structure of your data, allowing product metrics and financial KPIs to coexist in one system and speak the same language.
This runtime intelligently routes between local compute and remote systems, using query federation to analyze across sources without duplication. It syncs with your dbt repo and interprets your models in real time, extracting business context from transformation logic. Business concepts like MRR, churn, or feature adoption aren't just available: they're understood by the system itself.
That understanding is persistent and composable. Teams get a shared frame of reference, AI agents operate on semantically-rich data, and insights are always grounded in real definitions. For technical leaders, this removes a major pain point: aligning metrics across departments without sacrificing performance or control.
3. Autonomous Agents With Memory
Altertable doesn't wait for someone to ask the right question. Our agents operate continuously, scanning the data layer for unexpected changes, anomalies, correlations, and trends. But they go further: they remember.
Every insight is stored as a first-class object: what was found, what changed, what context was active, and what decisions followed. This history becomes an institutional memory layer that improves over time. Agents learn what's been acted on, what was noise, and how past changes influenced business outcomes.
This memory-driven approach makes the system more useful with every interaction. This means fewer blind spots, clearer narratives, and more explainability baked into the platform.
4. A True Dialogue With Your Data
Natural language is often treated as a surface layer for data platforms: a way to convert simple questions into SQL. Altertable treats it differently: as the foundation for an ongoing, adaptive dialogue between users and the system.
You can tell Altertable what matters to you. “Alert me if this feature tanks among new users.” “Let me know if churn increases among high-LTV cohorts.” These aren't hard-coded filters: they're behavioral inputs that shape how agents prioritize and surface information. You can ask follow-up questions, explore hypotheses, and see explanations tied directly to the semantic model.
Because this interface is grounded in lineage, definitions, and context, it doesn't hallucinate: it converses. Over time, it becomes a persistent interface layer that lets every stakeholder engage with data on their own terms, while reinforcing shared understanding.
The System We Always Needed
We're building a data OS because we've seen what it's like to scale companies on stacks that weren't meant to scale insight. At Algolia and Sorare, we lived through the pain of reactive dashboards, disconnected tools, and six-figure infrastructure budgets chasing basic answers. We knew patching the stack wasn't enough. We needed a system that could think, connect, and act.
Altertable is that system:
- A foundation where insight is continuous, not occasional.
- A system that understands your business, not just your data.
- A platform designed for action, not just observation.
It's the data operating system we always wished we had... and now we're building it for the next generation of teams.
